Health care, and especially access to health care, has always been a critical metric for countries. In 2017, South Africa spent 9% of its GDP on health care. Despite the GDP health care allocation being 5% higher than recommended by the World Health Organisation for a country of its socio-economic status, South Africa's health status is poor compared to similar countries. In 1994, South Africa implemented a health care policy to make health care accessible to all South Africans. A primary health care facility within 5km of the place of residence is deemed accessible.
There is still a significant gap between the actual and desired accessibility, especially for the lower-income communities. There is a need to improve access to public health care for all South Africans. Cost-effective and sustainable solutions are required to solve this problem. Therefore, an opportunity was identified to investigate the location of low-cost container clinics in lower-income communities.
This report uses robust optimisation and goal programming to find robust sites for cost-effective container clinics over multiple years in an uncertain environment using multiple future city development scenarios. The study area of the report includes three metro municipalities (City of Tshwane, City of Johannesburg, and City of Ekurhuleni) in Gauteng, South Africa. Three future development scenarios were created for this study using a synthetic population and urban growth simulation model developed by the CSIR. The model provided the population distribution from 2018 to 2030 for all three of the scenarios.
The simulation model provides household attribute tables as an output. Household attributes that have a causal relationship with health care demand were investigated during the literature review. Based on the literature and the available household attributes, four attributes were selected to forecast the health care demand. The four attributes are household income, the number of children in the household, the household size, and the nearest clinic's distance.
Using associative forecasting, the primary health care demand was forecasted from 2018 to 2030. These forecasts were used as input into the facility location models. A p-median facility location model was developed and implemented in Python. Since facility location problems are classified as NP-hard problems, heuristics and metaheuristics were investigated to speed up the problem solving. A GA selected as the metaheuristic be used to determine a suitable configuration of facilities for each scenario. The model determined good locations of clinics from a set of candidate locations. A good year to open each clinic is also determined by the model. These decisions are made by minimising three variables: total distances travelled by the households to their nearest clinics, the total distance from the selected distribution centre to the open clinics and the total building cost. An accessibility target of 90% was added to the model to ensure that at least 90% of the households are within 5km of the nearest clinic within the first five years. In these models, operating costs were not included. Therefore all the results are skewed, with most of the clinics being opened in the first year when it is the cheapest since there is no penalty for opening a clinic before it is needed — the exclusion of operating costs is a shortcoming to address in future work.
A goal programming model was developed with the variables of the individual scenarios as the goals. The goal programming model was implemented in Python and used to determine a robust configuration of where and in what year to open container clinics. A difference of 25% was set as the upper limit for the difference between the robust configuration variables and the good or acceptable variables for the individual scenarios as the scenarios investigated are very different. This ensured that the robust solution would perform well for any of the three scenarios. The model was able to find locations that provided a relatively good solution to all the scenarios. This came with a cost increase, but that is a trade-off that must be made when dealing with uncertainty. This model is a proof of concept to bridge the gap between urban planning with multiple development scenarios and facility location, more specifically robust facility location.
The biggest rendement was achieved by constructing and placing the container clinics in the shortest space of time because the 90% accessibility requirement can be addressed cost-effectively without an operating cost penalty ― this is unfortunately not possible in reality due to budget constraints. An accessibility analysis was conducted to investigate the impact of the accessibility percentage on the variable values and to test the model in a scenario closer resembling the real world by adding a budget constraint. The time limit of the accessibility requirement was removed. In this case, a gradual improvement in the accessibility over the 12 years was observed due to the gradual opening of clinics over the years. Based on the analyses results, it was concluded that the model is sensitive to changes in parameters and that the model can be used for different scenarios. / Dissertation (MEng (Industrial Engineering))--University of Pretoria, 2021. / Industrial and Systems Engineering / MEng (Industrial Engineering) / Unrestricted
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:up/oai:repository.up.ac.za:2263/81141 |
| Date | January 2021 |
| Creators | Karsten, Carike |
| Contributors | Bean, Wilna, u15012396@gmail.com |
| Publisher | University of Pretoria |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Rights | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
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